6 citations
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September 2025 in “Scientific Reports” This study found that using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.
19 citations
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October 2024 in “BMC Medical Informatics and Decision Making” This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.
2 citations
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January 2018 in “Springer eBooks” The document explains what healthy skin, nails, and hair look like on an ultrasound.
2 citations
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September 2023 in “JMIR. Journal of medical internet research/Journal of medical internet research” This study reported that AutoML effectively modeled itching and pain development, as well as app use, in patients with chronic eczema or psoriasis using a smartphone monitoring app, revealing that factors like BMI, age, and disease activity significantly influenced app engagement.
1 citations
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September 2025 in “Journal of Ultrasound in Medicine” This study found that AI using YOLOv11 architecture can reliably differentiate between hyaluronic acid and silicone oil cosmetic fillers on ultrasound, achieving high accuracy, whereas identifying calcium hydroxyapatite and polymethylmethacrylate remains less consistent, requiring further improvements.